Project 3: Dissecting p53 Action in a Mouse Lymphoma Model Scott Lowe, Ph.D. p53 is a potent tumor suppressor that limits cancer development and contributes to the anti-tumor effects of certain cytotoxic agents. Accordingly, p53 mutations are associated with aggressive cancers, poor patient prognosis and, in some cases, therapy resistance. Our project studies the p53 network using the Ep-myc transgenic mouse, a well characterized model of B cell lymphoma in which p53 profoundly impacts tumorigenesis and treatment outcome. We previously used the Emu-myc system to study how p53 loss promotes tumor development and drug resistance, and to explore the relationship between cancer genotype, tumor progression, tumor maintenance, and treatment sensitivity. These studies produced new insights into p53 regulation and effector mechanisms in diverse contexts where it limits proliferation. They also established that p53 loss is required for tumor maintenance and that translational control of cell survival is a druggable mediator of oncogenesis. To facilitate our goals, we developed methods to genetically manipulate Emu-myc lymphomas, enabling the rapid and cost-effective production of mice harboring tumors with diverse genotypes. We also incorporated small animal imaging methods to track lymphoma progression and response, and stable and inducible RNAi technology to fine tune and study loss of function phenotypes. Moving forward, we seek to further explore the interplay between cancer genetics and cancer therapy in the Emu-myc system in order to produce a more complete picture of the p53 network and its action in vivo. We also wish to understand in detail how p53 mutations impact tumor behavior, and identify new therapeutic targets based on the vulnerabilities they create. We will perform focused RNAi screens to identify new modulators of p53 action relevant to suppressing tumorigenesis and/or modulating therapy response, test the hypothesis that the deletions on chromosome 17p influence cancer biology beyond simply inactivating p53, and identify and characterize drug targets that interact with the p53 tumor suppressor network. Our approach uses genomic data from human lymphoma to inform function studies in mice, and implements a suite of new animal modeling and genetic tools that are time and cost effective.